Price bias and common practice in option pricing

Generally, the semiclosed-form option pricing formula for complex financial models depends on unobservable factors such as stochastic volatility and jump intensity. A popular practice is to use an estimate of these latent factors to compute the option price. However, in many situations this plug-and-play approximation does not yield the appropriate price. This article examines this bias and quantifies its impacts. We decompose the bias into terms that are related to the bias on the unobservable factors and to the precision of their point estimators. The approximated price is found to be highly biased when only the history of the stock price is used to recover the latent states. This bias is corrected when option prices are added to the sample used to recover the states' best estimate. We also show numerically that such a bias is propagated on calibrated parameters, leading to erroneous values. The Canadian Journal of Statistics 48: 8–35; 2020 © 2019 Statistical Society of Canada     

内核级并发通信的研究

提出了一种内核级并发消息通信机制。该机制采用对象传送协议和动态线程池技术,并通过会话控制完成数据的收发过程,将线程池设计为二级阻塞队列来暂缓线程的撤销过程,从而保证既能够快速响应客户请求,又可减少因频繁创建和撤销线程而消耗系统的资源和时间,为分布式操作系统提供了高效可靠的通信服务。     

Using local linear kernel smoothers to test the lack of fit of nonlinear regression models

Herein, we propose a data-driven test that assesses the lack of fit of nonlinear regression models. The comparison of local linear kernel and parametric fits is the basis of this test, and specific boundary-corrected kernels are not needed at the boundary when local linear fitting is used. Under the parametric null model, the asymptotically optimal bandwidth can be used for bandwidth selection. This selection method leads to the data-driven test that has a limiting normal distribution under the null hypothesis and is consistent against any fixed alternative. The finite-sample property of the proposed data-driven test is illustrated, and the power of the test is compared with that of some existing tests via simulation studies. We illustrate the practicality of the proposed test by using two data sets.     

Kernel Estimation of Rate Function for Recurrent Event Data

Abstract.  Recurrent event data are largely characterized by the rate function but smoothing techniques for estimating the rate function have never been rigorously developed or studied in statistical literature. This paper considers the moment and least squares methods for estimating the rate function from recurrent event data. With an independent censoring assumption on the recurrent event process, we study statistical properties of the proposed estimators and propose bootstrap procedures for the bandwidth selection and for the approximation of confidence intervals in the estimation of the occurrence rate function. It is identified that the moment method without resmoothing via a smaller bandwidth will produce a curve with nicks occurring at the censoring times, whereas there is no such problem with the least squares method. Furthermore, the asymptotic variance of the least squares estimator is shown to be smaller under regularity conditions. However, in the implementation of the bootstrap procedures, the moment method is computationally more efficient than the least squares method because the former approach uses condensed bootstrap data. The performance of the proposed procedures is studied through Monte Carlo simulations and an epidemiological example on intravenous drug users.     

中国医疗机构定价中的问题及对策建议

医疗机构医疗服务定价需要考虑因素是医疗卫生行业管理的一个重要内容,合理有效定价对实现社会公平和福利分配,保障居民生命健康、促进社会稳定的具有重要的现实意义。本文对国内外医疗定价的几种主要模式进行简要介绍,然后分析一下我国现行医疗服务定价存在问题的原因,并给出几点改革建议,讨论需要建立一个合理的定价体系和监管体系需要考虑因素。     

A Semi-parametric Regression Model with Errors in Variables

Abstract.  In this paper, we consider a partial linear regression model with measurement errors in possibly all the variables. We use a method of moments and deconvolution to construct a new class of parametric estimators together with a non-parametric kernel estimator. Strong convergence, optimal rate of weak convergence and asymptotic normality of the estimators are investigated.     

Deconvolution with supersmooth distributions

Nonparametric deconvolution problems require one to recover an unknown density when the data are contaminated with errors. Optimal global rates of convergence are found under the weighted L_p-loss (1 ≤ p ≤ ∞). It appears that the optimal rates of convergence are extremely low for supersmooth error distributions. To resolve this difficulty, we examine how high the noise level can be for deconvolution to be feasible, and for the deconvolution estimate to be as good as the ordinary density estimate. It is shown that if the noise level is not too high, nonparametric Gaussian deconvolution can still be practical. Several simulation studies are also presented.     

国际转移定价及其税务规制研究

我国加入WTO后,跨国公司大举进军中国市场,国际转移定价滥用问题日益突出。我国应该采取措施,强化转移定价滥用行为监督管理意识,完善转移定价税制,建立和健全审计制度,改善监管手段,同时加强国际合作,加大对转移定价滥用行为的监管和惩处力度。     

市场结构与企业定价研究

本文首先从分析影响企业定价的市场结构入手 ,研究了处于不同市场结构环境中的企业定价战略 ,继而结合案例分析了不同市场结构的企业定价策略和技巧 ,特别是给出了几种实用的定量定价方法。在研究各种定价策略的基础上 ,提出了一个实用有效的定价程序。此外 ,本文还用博弈论的观点重点分析了寡头垄断市场的定价策略     

高校资产证券化探讨

高校扩招造成了教学经费不足。目前在全国几乎找不到没有银行贷款的高校，随着“银校间热恋的甜蜜时期”的结束，如何如期还贷，如何进一步融资成为众多高校重大的财政问题，本研究给出了高校收费资产证券化方法，不仅将未来缺乏流通的现金流变现，解决了未来现金流收入与还贷期限结构的不匹配，而且融资成本低。本研究给出了资产证券化的一般组织结构和流程，并对我国高校收费证券化进行了定价。